Comparing the performance of winsorize tree to other data mining techniques for cases involving outliers

Abstract

Winsorize tree is a modified tree that reformed from classification and regression tree (CART). It lays on the strategy of handling and accommodating the outliers simultaneously in all nodes while generating the subsequence branches of tree. Normally, due to the existence of outlier, the accuracy rate of most of the classifiers will be affected. Therefore, we propose winsorize tree which could resist to anomaly data. It protects the originality of the data while performing the splitting process. In this study, winsorize tree was compared to other classifiers. The results obtained from five real datasets indicate that the proposed winsorize tree performs as good as or even better compare to the other data mining techniques based on the misclassification rate.